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Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy

ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the...

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Autores principales: Cheng, Qiong, Kazemian, Majid, Pham, Hannah, Blatti, Charles, Celniker, Susan E., Wolfe, Scot A., Brodsky, Michael H., Sinha, Saurabh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731213/
https://www.ncbi.nlm.nih.gov/pubmed/23935523
http://dx.doi.org/10.1371/journal.pgen.1003571
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author Cheng, Qiong
Kazemian, Majid
Pham, Hannah
Blatti, Charles
Celniker, Susan E.
Wolfe, Scot A.
Brodsky, Michael H.
Sinha, Saurabh
author_facet Cheng, Qiong
Kazemian, Majid
Pham, Hannah
Blatti, Charles
Celniker, Susan E.
Wolfe, Scot A.
Brodsky, Michael H.
Sinha, Saurabh
author_sort Cheng, Qiong
collection PubMed
description ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called “STAP,” to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed (“primary”) TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ≤150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA.
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spelling pubmed-37312132013-08-09 Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy Cheng, Qiong Kazemian, Majid Pham, Hannah Blatti, Charles Celniker, Susan E. Wolfe, Scot A. Brodsky, Michael H. Sinha, Saurabh PLoS Genet Research Article ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called “STAP,” to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed (“primary”) TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ≤150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA. Public Library of Science 2013-08-01 /pmc/articles/PMC3731213/ /pubmed/23935523 http://dx.doi.org/10.1371/journal.pgen.1003571 Text en © 2013 Cheng et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Cheng, Qiong
Kazemian, Majid
Pham, Hannah
Blatti, Charles
Celniker, Susan E.
Wolfe, Scot A.
Brodsky, Michael H.
Sinha, Saurabh
Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy
title Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy
title_full Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy
title_fullStr Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy
title_full_unstemmed Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy
title_short Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy
title_sort computational identification of diverse mechanisms underlying transcription factor-dna occupancy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731213/
https://www.ncbi.nlm.nih.gov/pubmed/23935523
http://dx.doi.org/10.1371/journal.pgen.1003571
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